Image segmentation is a branch of digital image processing that performs the task of categorizing, or classifying, the elements of a digital image into one or more class types. For medical imaging applications, it is common that image segmentation is performed on the voxels (volume elements) of a 3-dimensional image data set with the classification types related to anatomical structure. One task is segmenting regions within the image data set that are considered as cancerous or diseased tissue. This capability enables physicians and radiologists to diagnosis, to plan treatment, and to assess the progress of treatment in a rapid and perhaps more effective manner than a manual process.
Several systems and/or methods for segmenting diseased tissue on a PACS (Picture Archive and Communication System) are known. Such systems and/or methods initiate segmentation by having the physician point to the location of the diseased tissue using a pointing device such as a mouse. The pointing is achieved by a click of one of the buttons on the mouse. A segmentation algorithm is launched, resulting in the outline and/or three-dimensional rendering of the volume of diseased tissue being identified. Subsequently, the result of the segmentation maybe drawn for the user and the volume of the segmented is reported.
A typical image segmentation strategy is generally known as “region growing.” Starting with a seed point (i.e., a voxel position that is known to be part of a particular class type), a region of contiguous voxels is grown, or expanded about the seed point. The region growing process progresses until a stopping condition is satisfied, e.g., no more contiguous voxels are found, or a predetermined number of voxels have been visited, etc. Often the stopping conditions are based upon a threshold condition. Such a condition is the voxels have a signal value (e.g., Hounsfield units for CT) greater than the threshold. The region continues to expand as long as the adjoining voxels comply with the threshold condition. In some cases, this simple stopping condition leads to unconstrained growth, as the signal values of diseased tissue and healthy tissue are similar. This results in additional constraints, such as maximum distance away from the initial seed point.
An approach to form a bounding box is for the user to input two or more points and a rectilinear volume is specified by the minimum and maximum coordinates of the points in each dimension. The resulting eight points form the vertices of the rectilinear volume. The box is defined as a set of voxels v=(v1, v2, v3) with the property that |v1−c1|≦d1, |v2−c2|≦d2 and |v3−c3|≦d3 where c=(c1, c2, c3) is the center of the bounding box and d=(d1, d2, d3) is half of the width, length and height of the box. This has the advantage of being simple to implement and the bounds check is also trivial. Unfortunately, the approach results in many voxels that could be readily ignored, and the boundary of the box does not reflect anatomical shapes, which are generally smooth without sharp rectilinear corners.
An approach to reporting the size of the diseased tissue is given by the Response Evaluation Criteria In Solid Tumors (RECIST) method. In such a method, a physician first identifies the slice in the medical image, e.g., computed tomographic exam, having the largest cross-section. Then within that slice two points are identified forming the longest section across the diseased tissue. The length of the line is commonly referred as the diameter of the diseased tissue, or more often as the size of the diseased tissue. It is understood that this measure is not precise, as many regions of diseased tissue are not spherical and the longest diameter may also not be within a single axial slice.
It is known that a human has unexcelled capability to interpret visual information, even when the information is obscured or ambiguous. The advantage of semi-automatic systems, i.e., systems that use both human and machine capabilities, is that the relative strengths of humans and machines are utilized. Limited information about the diseased tissue, such as the location of diseased tissue, or a bounding box containing the diseased tissue, is often easier and more dependable for the human to provide than the machine. However, identifying all the voxels within the diseased tissue is tedious and often accomplished better by machine. The human is also able to determine nuances that alter a preferred strategy for achieving a desired outcome and these are often difficult for a machine to perform.
While the automatic sizing approach with a single point will produce useful results, it may still be difficult to distinguish between diseased and normal tissues having the same signal levels within the examination type, e.g., computed tomography. A bounding box is a means to address this problem. Since diseased tissue's morphology can be irregular the ability to discern the boundary of the diseased and normal tissue depends upon the intelligence incorporated into the segmentation algorithm.
However, diseased tissue tends to grow from a small region or point. The growth of the disease tends to be more spherical than rectilinear. Thus, there is a need for is a fast and convenient way for a radiologist to input more useful information to a segmentation algorithm that is consistent with morphology of diseased tissue growth. For example, while a single position of the diseased tissue is a minimal interaction, there are cases, as previously described, where a single point does not yield satisfactory results. Often by providing slightly more initial data to the algorithm, the problem can be reduced or eliminated.